app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "norman-codes/transfer-learning-attempt1"

# Load the model and tokenizer from Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

st.title("Text Generation with GPT-Neo")
st.write("This is a text generation model running on Hugging Face Spaces. Enter a prompt to generate text.")

prompt = st.text_input("Enter your prompt here:")

if st.button("Generate Text"):
    with st.spinner("Generating..."):
        # Encode the input prompt and generate text
        input_ids = tokenizer(prompt, return_tensors="pt", add_special_tokens=True).input_ids
        generated_ids = model.generate(input_ids, max_length=100)
        generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)

    st.text_area("Generated Text:", value=generated_text, height=200, max_chars=None, key=None)